Spectrum sweeping devices are used to detect signals transmitted within a frequency band. Some spectrum sweeping applications require a search for signals over a wide bandwidth, in which case the time required for a sweep of the frequency band might be considerable. This can be problematical when fast detection of signals is needed, so that a response can be provided quickly with real-time signal processing. One way to reduce the required time is to utilize multiple receivers or channelizers to allow portions of the frequency band to be swept in parallel, but such systems may be expensive to set up and require substantial computing resources.
Compressed sensing schemes can be used to reconstruct a detected signal using a reduced number of samples, under an assumption that the signal is sparse. The sampling rate required by such schemes may be less than the rate predicted by the Nyquist sampling theorem. However, compressed sensing schemes may have high computational complexity, making them less useful for applications requiring real-time signal processing. Such compressed sensing schemes are also limited to specific classes of signals, since they assume a specific sparsifying transfer domain, and do not allow for simultaneous reconstruction of multiple unknown parameters.
Also, parameter estimation algorithms used to determine characteristics of detected signals may be subject to other limitations. For example, the Multiple Signal Classifier (MUSIC) algorithm can be used to determine the angle-of-arrival of uncorrelated signals, but does not handle multipath signals effectively.